1/ 🎉 Day 8 of #AllinAI: As someone who's always been fascinated by #AR#VR and 3D scanning, I'm excited to explore Neural Radiance Fields (#NeRF)! NeRF is a game-changing AI technique that has transformed the 3D scene reconstruction & rendering. Let's dive in!
2/ 📊 Traditional 3D scanning methods like #photogrammetry & #lidar faced challenges like noise, inconsistencies & limited detail. NeRF uses AI to help overcome these by creating high-quality 3D renderings from 2D images.
3/ 🌐 How it works: Take a few pictures of an object. NeRF's deep neural network optimizes the radiance field to match the 2D image, combining color & volume density to create a stunning, detailed 3D rendering.
4/ 💡 NeRF's value proposition: Faster and more cost-effective 3D content creation in industries like gaming, film production, and AR/VR. NeRF is transforming the way we create and experience immersive content.
5/⚠️ Limitations: NeRF might not be suitable for micron-level measurements (printing medical devices, aerospace parts, etc.), but it shines in gaming, entertainment, and other fields where these limitations matter less.
6/ 📚 In fact, NeRF's visual & geometric likeness compared to ground truth scans has been demonstrated in papers by research teams at @Stanford@UCBerkeley@Google Research.
7/ Check out these companies doing cool stuff with NERFs: @DeepMap builds 3D maps, Nerf Labs builds NeRF-based toys & games, and @LumaLabsAI uses NeRFs to capture and experience the world in 3D.
8/ NeRF's potential to revolutionize 3D content creation is immense. If you're working on NeRF, @RevelryVC would love to chat with you! Stay tuned for more updates on #AllinAI. #StayCurious
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1/ Day 7 of #AllInAI: Generative Adversarial Networks (#GANs)
Yesterday, we explored synthetic data, which led us to GANs - the tech behind creating realistic synthetic data. After learning more, there are several other real world applications that have come from GANs research… twitter.com/i/web/status/1…
2/ 👊GANs, or Generative Adversarial Networks, are a type of AI model architecture/technique. They consist of two separate neural networks, a Generator and a Discriminator, that are trained together in a process called Adversarial Training. The goal is to generate new, realistic… twitter.com/i/web/status/1…
3/ 🤖✍️ Imagine a GAN model generating realistic human signatures. The Generator creates a random signature, while the Discriminator classifies it as "real" or "fake." They improve over time, and eventually, the Generator creates signatures that look authentic & are hard to… twitter.com/i/web/status/1…
1/ Day 6 of #AllinAI 🧪 Introducing #SyntheticData - an incredibly powerful tool that's quickly becoming a must-have for AI teams. Synethic data can help companies overcome some of the key issues of AI & data collection, such as privacy, biases, and data scarcity.
2/ 💡 What is Synthetic Data? It's artificially generated data that mimics the characteristics of real-world data. It's created using algorithms, simulations, and generative models like GANs (which pit AI vs. AI to create and authenticate "fake" data).
3/ 📈 Why companies should consider Synthetic Data in their AI stack:
1. Reduces data privacy concerns 2. Helps overcome data scarcity 3. Enhances dataset diversity 4. Reduces biases in data 5. Facilitates edge cases testing 6. Speeds up model development
Open sourcing @RevelryVC's AI Data Strategy DD Checklist.
1/ Data is the fuel for building AI so we're sharing our "AI Data Strategy" DD Checklist to gather feedback and insights to improve our evaluation process and provide value to other investors and founders. #AllInAI
2/ We've identified 7 key areas to focus on when evaluating a startup's data strategy. These areas are critical to ensure that the AI system is effective and that the company's approach aligns with its overall goals.
Here are the 7 areas of our DD process:
3/ Data Acquisition: The process of collecting, sourcing, and obtaining relevant data. A strong data acquisition strategy ensures a diverse and reliable dataset that accurately represents the target use case and can be a critical moat over time.
🧠 While @OpenAI grabs headlines, we wanted to dig into @DeepMind, one of the OG AI research institutions acquired for ~$500M by @Google (a steal!). Its breakthroughs have made a huge impact on the AI field. Let's take a look! 🚀 #AllInAI (1/9)
🎮 @DeepMind's AlphaGo made history in 2016 by defeating Lee Sedol in Go. Combining deep learning & reinforcement learning with Monte Carlo Tree Search, AlphaGo soon learned to play complex games entirely through self-play, without human guidance. (2/9)
🎼 WaveNet, another @DeepMind invention, uses a convolutional neural network (CNN) to generate realistic human-like speech. It powers text-to-speech applications like @Google Assistant and has pushed the boundaries of speech synthesis, music generation, and voice cloning. (3/9)